WRANGLERS
Photo by Alex Alvarez on Unsplash
The World Happiness Report has proven to be an indispensable tool for policymakers
looking to better understand what makes people happy…
— Jeffrey Sachs
df <- read_xls('./archetypes/happiness-report/happiness-report-2020.xls')
df
dim(df)
## [1] 1704 26
The output tells us that the data contains 1704 rows, and 26 columns.
df1 <- df[,3:ncol(df)]
nRows <- dim(df1)[1]
calcStats <- function(x) {
temp <- na.omit(df[, x])
pos <- sum(temp > 0)
is_zero <- sum(temp = 0)
neg <- sum(temp < 0)
c("number of positives" = pos, "negatives" = neg, "zero" = is_zero)
}
result <- as.data.frame(Map(calcStats, colnames(df1)))
result
df_long <- df %>%
pivot_longer(
`Life Ladder`:`Most people can be trusted, WVS round 2010-2014`,
names_to = "measure",
values_to = "value"
)
v1 <- ggplot(df_long, aes(x=value)) +
geom_histogram(fill = "#79B8E5") +
facet_wrap(~ measure, scales="free")+
theme(panel.grid = element_blank(),
strip.background = element_blank(),
panel.background = element_blank()
)
girafe(ggobj = v1, width_svg = 16, height_svg = 9, options =
list(opts_sizing(rescale = TRUE, width = 1.0))
)
Just looking at the graph, we see that the “Life Ladder” varies from 0 to 8, with the majority of values between 4 and 6. “Log GDP per capita” varies from about 5 to 12. So the GDP per person would vary in this dataset from $150 to $160k.
@misc{helliwell_2019_world,
author = { Helliwell, John F. Helliwell and Layard, Richard and Sachs, Jeffrey D. },
title = {World Happiness Report 2019},
url = {https://worldhappiness.report/ed/2019/},
urldate = {2021-05-18},
year = {2019},
organization = {Worldhappiness.report}
}